Execution Models for Distributed Computing - 2nd generation

This lecture covers the evolution of execution models for distributed computing, focusing on the 2nd generation. It discusses the limitations of MapReduce, the introduction of Spark, and the concept of Resilient Distributed Datasets (RDDs). The lecture explores the architectural choices of Spark, the benefits of RDDs over Hadoop HDFS, and the design principles of big data systems. It also delves into resource management in distributed environments, fault tolerance strategies, and job recovery mechanisms in Spark.